Research Article
Exploration of Machine Learning Applications in Systemic Financial Risk Prediction and Management
@INPROCEEDINGS{10.4108/eai.8-12-2023.2344705, author={Keyu Yao}, title={Exploration of Machine Learning Applications in Systemic Financial Risk Prediction and Management}, proceedings={Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8--10, 2023, Guangzhou, China}, publisher={EAI}, proceedings_a={MSIEID}, year={2024}, month={4}, keywords={financial risk management machine learning techniques transparency systemic risk warning deep learning}, doi={10.4108/eai.8-12-2023.2344705} }- Keyu Yao
Year: 2024
Exploration of Machine Learning Applications in Systemic Financial Risk Prediction and Management
MSIEID
EAI
DOI: 10.4108/eai.8-12-2023.2344705
Abstract
In this multidisciplinary study, we explore the transformative impact of machine learning (ML) technologies in financial research. Our objective is to understand how supervised and unsupervised learning methods can be applied to tasks such as fraud detection, asset price forecasting, financial risk assessment, and the development of early warning systems for systemic financial risk. We adopt a variety of algorithms—including Backpropagation Neural Networks, Bayesian Networks, Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), Random Forest, and XGBoost—and evaluate their ability to analyze and predict outcomes from complex financial data. Our methodology entails a comparative analysis of ML techniques against traditional statistical methods, particularly in their handling of imbalanced datasets.
